Coupling Large-Scale Omics Data for Deciphering Systems Complexity

Abstract

Recent development in high-throughput experiments has provided great amount of data that is being used in translational personalized medicine. Data available in public databases is increasing exponentially as a result of the progress in omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Advancements in computing power and machine intelligence are affecting large-scale data analysis and integration. Two types of data integration are often considered: horizontal and vertical meta-analysis. The former integrates multiple studies of the same type, while the latter integrates data at different biological levels. This integrative approach provides a better understanding of systems complexity as a result of the global view that it offers from a biological point of view. This chapter describes the different types of omics analysis and discusses the methods of integrating multi-omics data using a case study.